This paper proposes a moving car segmentation method based on improved OTSU method.
提出一种基于改进OTSU方法的运动车辆分割算法。
Otsu method is a common and efficient image segmentation algorithm, and has been used in many various real-time systems.
最大类间方差法是一种常用而有效的图像分割算法，并已在许多实时系统中采用。
Based on the Rough Set Theory, the image data is simplified and classified into targets and backgrounds with the combination of the OTSU method.
在图像中利用粗糙集理论对图像特征数据进行有效约简，并和最大类间方差法结合，将图像分为目标和背景两部分。
The comparison between this method and the 1-dimensional OTSU method show that this method performs much better when the images are corrupted by noise.
通过与一维的Otsu法比较表明，在有噪声的图象中，本文提出的方法性能好得多。
The method avoids large amount of variance calculation and its calculation quantity is only about 50% that of Otsu method. It can fulfill the demands of adaptability and real time.
该方法避免了大量的方差计算，计算量仅为大津法的50%左右，能够满足自适应性和实时性的要求。
One of the most useful thresholding techniques using gray-level histogram of an image is the OTSU method. The objective of this paper is to extend it to the 2-dimensional histogram.
Otsu法是最常用的利用图象一维灰度直方图的阈值化方法之一，本文的目的是将它推广到二维直方图。
Compared with the Mean Gray Level and OTSU algorithm, the binary conversion method has advantages of processing small-area defect images.
同otsu算法和灰度平均值算法比较，该图像二值化方法在具有小区域缺陷特征图像处理方面有一定的优势。
Using the maximum between-class variance method (OTSU) obtained image binarization best treatment threshold, and thus image binarization treatment.
利用最大类间方差法（OTSU）求出对图像进行二值化处理的最佳阈值，从而进行图像二值化处理。
Thirdly, according to the differences of target images and spot images in gray-scale features, Otsu adaptive segmentation method is adopt to realize the image segmentation.
接着根据靶标图像和光斑图像在灰度特征上的差异，采用了最大类间方差自适应分割法实现了光靶图像的分割。
By analyzing the traditional thresholding methods (such as Otsu and Kapur), a new approach using the adaptive thresholding method is presented.
针对金相图中分割问题，在分析对比传统的全局阈值分割方法的基础上，提出了一种自适应阈值分割方法。
OTSU threshold method has a very wide range of applications in the image segmentation for its simplicity and intuitive.
OTSU阈值方法以其简洁性和直观性在图像分割中有着十分广泛的应用。
The traditional OTSU threshold method has been improved in order to establish a good foundation for feature extraction and the fitting of 3D in image process with calibration plate.
本文利用改进的OTSU阈值方法测量人体空间三维尺寸的带标定框图像，为后续的特征点提取和三维拟合计算打下良好的基础。
Aim at removing the background noise in the large measuring range system (about 2m), a method which combines Otsu based on ROI and gray barycenter method is proposed.
针对图像背景噪声强的特点，提出了一种基于感兴趣区域(ROI)的结构光条纹中心处理方法，即在ROI内用最大类间方差法进行阈值分割再用重心法进行细化。
Aim at removing the background noise in the large measuring range system (about 2m), a method which combines Otsu based on ROI and gray barycenter method is proposed.
针对图像背景噪声强的特点，提出了一种基于感兴趣区域(ROI)的结构光条纹中心处理方法，即在ROI内用最大类间方差法进行阈值分割再用重心法进行细化。
应用推荐